Enhanced Red-tailed Hawk Algorithm: Elevating Cloud Task Scheduling Efficiency

Abstract

Abstract With the popularity of cloud computing, effective task scheduling has become the key to optimizing resource allocation, reducing operation costs, and enhancing the user experience. The complexity and dynamics of cloud computing environments require task scheduling algorithms that can flexibly respond to multiple computing demands and changing resource states. To this end, this study proposes an improved RTH algorithm, the ERTH algorithm, which aims to improve the efficiency and effectiveness of task scheduling in cloud computing environments. Evaluations in the CEC benchmark test sets show that the ERTH algorithm outperforms the traditional PSO and GWO in several performance metrics and outperforms the emerging GWCA and CSA. This result signifies a significant advancement of the ERTH algorithm in intelligent optimization. Further, we apply the ERTH algorithm to a real cloud computing environment and conduct a comparison with the original algorithm RTH, PSO, ACO, WOA, and HLBO. When dealing with cloud computing task scheduling problems, the ERTH algorithm demonstrates better task completion time, resource utilization, and system load balancing performance. Especially in high-load and complex task scenarios, the stability and scalability of the ERTH algorithm perform exceptionally well. This study not only reveals the powerful potential of the ERTH algorithm in cloud computing task scheduling but also brings new perspectives and solutions for cloud service providers in resource allocation and task scheduling strategies. The proposal and validation of the ERTH algorithm are of great significance in promoting the application of intelligent optimization algorithms in cloud computing.